Daily AI Paper
Every day, our AI scans the latest arXiv submissions, picks the most notable one, and narrates a video introduction — automatically.
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2026-09-26PoEM: Predicting RL Outcomes from Existing Policies
Kimia Hamidieh, Giannis Daras, Antonio Torralba
arXiv:2609.30226v1A major bottleneck in reinforcement learning is that every new reward usually means running a fresh, expensive training loop from scratch. This paper asks a simpler question: if we already have a set of models trained on different rewards, can we predict what RL would produce for a new reward without retraining? The authors introduce PoEM, which treats existing post-trained policies as a basis. In the ideal case, if the new reward is just a combination of old ones, the new policy can be approximated as a matching combination in log space. More surprisingly, they find that even when rewards are not explicitly related, the learned policies often still lie near a low-rank subspace. That means the new policy can be estimated from existing ones by fitting a small set of coefficients from reward or policy outputs. If this works broadly, it could make policy iteration far cheaper, faster, and more modular for language and vision models that need continual post-training.
Also spotted that day
LLM Agents Can Easily Tamper With Their Own TracesAD-WM: Action-Discriminative World Models for Counterfactual Model Predictive ControlTemporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement LearningAgentic Detection of Online ConspiraciesRAPID: Robot Agentic Programming from DemonstrationsRolling-WAM: World Action Models with Rolling ImaginationJevOut: Natural Context Can Flip Decision ModelsSemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete DataCoding Agents for Generalized Task and Motion Planning ProblemsTo Trust or Not to Trust: Retrieval-Augmented Fact Checking in SpeechTrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene RepresentationsRequirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release AuthorityMinimally Invasive Steering of Language ModelsInstrumental Monitor Evasion Emerges Under Ordinary Task Pressure
Previous daily papers
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